DOCA: a UAV-assisted multi-hop D2D resource allocation scheme for 5G and beyond using machine learning

Abstract

Device-to-Device (D2D) relayed communication helps in extending the coverage range of cellular networks. Relay devices support multi-hop D2D communication where two devices are out of the direct D2D range. However, identifying suitable fixed relays in a network is a complex problem that needs a more efficient solution. Relay-assisted communication may also fail due to the non-cooperative nature of the users (draining battery energy for supporting other devices). This paper proposes a UAV (Unmanned Aerial Vehicle)-assisted multi-hop D2D communication scheme that serves more out-of-direct-range D2D users using the dynamic location of the UAVs (drones). Dynamic location of UAVs solves the connectivity issues with many users. We aim at maximizing the achievable throughput of the D2D users for both uplink (users to UAVs) and downlink (UAVs to users) channels simultaneously. An optimization problem is formulated for maximizing throughput subject to interference, power, and bandwidth constraints. The UAV trajectories are predicted for serving the multi-hop D2D users in the system using Neural Network (NN), and thereafter, a novel resource assignment scheme, named Dual Optimal Channel Allocation (DOCA), is proposed. DOCA optimally allocates resource blocks (RBs) for both uplink and downlink channels and ensures that the overall interference caused by resource sharing between cellular and D2D users is minimal. The spectrum efficiency has been achieved by resource sharing between cellular and D2D users. An association matrix is obtained that indicates potential resource-sharing partners of D2D and cellular users. Finally, we show the performance of the proposed technique with regard to throughput improvement, buffer requirement, and churn rate of the system.